CIL Road Segmentation Project

This repository contains the code of our group project for the Computational Intelligence Lab (SS23 ETH Zürich).

Team Members:

• Dustin Brunner
• Michael Ungersböck
• Siu-Sing Yip
• Yanick Zengaffinen

Setup

We recommend using the conda package manager. Navigate to the CIL2023 folder and run:
conda create --name cil python=3.8
conda activate cil
pip install -r requirements.txt

Datasets

We are using the following datasets:

1. data5k: training dataset (2 x 5'000 images)
2. data30k: alternative training dataset (2 x 30'000 images)
3. training (from kaggle): validation dataset (144 images)
4. test500: test dataset (2 x 1'000 images)
5. test: submission dataset (144 images)

Reproduce Results

This is a step-by-step guide on how to reproduce our results. Reoccuring steps will be explained below. Before starting: download all data and ideally put them into a ./data folder (otherwise you'll have to adjust the paths in the configs). Ensemble experiments require you to be on the dustin/ensemble branch.

Random

1. Run python main.py generate-random data/test500 out/random/test500 to generate random masks.
2. Run & Evaluate on test500
3. Run & Submit test

U-Net++ R152 60k and D-LinkNet R152 60k

1. Select the corresponding config from ./report/configs and paste it into ./config.py
2. Train on data30k
3. Run & Evaluate on test500
4. Run & Submit test

Ensemble Baseline

Requires U-Net++ R152 60k, D-LinkNet R152 60k and 3 models each with only R50 backbone analogously (see ./report/configs/submodels)

1. Run all submodels on following datasets: training, test, test500, data5k
2. Train Ensemble based on submodel predictions (see ./report/configs/ensemble_baseline.py)
3. Run & Evaluate Ensemble on test500
4. Run & Submit test

VoteNet R50 10k

1. Run the ADW-Transform notebook on the training and validation data (adjust DATASET_FOLDER). This gives you the groundtruths of the 3 modalities (angle, distance, width)
2. Train a model for each of the 3 modalities (adjust the groundtruth_subfolder in config for each of them) (see ./report/configs)
3. Generate the mean and std for all 3 trained models on the validation and test data (adjust data_dir and select_channel [0 = mean, 1 = std] in config)
4. Run the ADW-Reconstruct notebook on test500 and test data. This gives you the mask priors.
5. Evaluate on test500
6. Submit test

Ensemble incl. VoteNet

Requires U-Net++ R152 60k, D-LinkNet R152 60k and 3 models each with only R50 backbone analogously (see ./report/configs/submodels). Additionally, requires VoteNet R50 10k.

1. Run all submodels on following datasets: training, test, test500, data5k (for VoteNet this implies transformation, running and reconstruction)
2. Train Ensemble based on submodel predictions (see ./report/configs/ensemble_baseline.py)
3. Run & Evaluate Ensemble on test500
4. Run & Submit test

Refined Ensemble incl. VoteNet

Requires a trained Ensemble incl. VoteNet

1. Generate the low quality masks using python main.py prepare-for-refinement (equivalent to calling run on all data)
2. Update config (see ./report/configs for the specifics). Notably IS_REFINEMENT = True
3. Train on data5k
4. Optional: Repeat steps 1 & 3 arbitrarily often
5. Run & Evaluate on data500
6. Run & Submit test

Individual Steps

Training

Trains a model and chooses the model with lowest validation loss.

1. Specify your model-, data- and train-configuration in the TRAIN_CONFIG dictionary in config.py
2. Execute python main.py train in the command line In case the training crashes: Set resume_from_checkpoint to True in the TRAIN_CONFIG and specify the full experiment_id (including the timestamp!) of the training run you want to continue.

Run

Generates the outputs of a trained model on a specified set of images.

1. Specify your run configuration in the TRAIN_CONFIG dictionary in config.py. Make sure the model config is consistent with the config used during training.
2. Execute python main.py run in the command line In case you prefer to use the last instead of the best model checkpoint, append a -l (see main.py for all options)

Evaluate

Computes Loss and F1-score (including std dev over all samples) for a specific model. Make sure you have run the model first or at least setup the proper config so it can run automatically!

1. python main.py evaluate

Submission

Generates submission masks and the submission.csv in the format specified by the kaggle competition.

1. python main.py submission "experiment_id" (include timestamp in experiment_id) In case you want to specify a different foreground threshold, use -t 0.5 for example.

To see a list of all available commands type:

python main.py --help

Image Collection

Additional training images can be collected using the src/image_collection.py script. The script requires a valid Google Maps API key in the GMAPS_API_KEY environment variable. Usage can be checked using python src/image_collection.py --help

Location Config

The location config acts as an input to the image collection process. The bbox property describes the bounding box of the specific location. The first value corresponds to the upper-left corner and the second to the lower-right corner.